Show code
library(GeoPressureR)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(plotly)
library(GeoLocTools)
setupGeolocation()
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/4_basic_graph/", params$gdl_id, "_basic_graph.Rdata"))
Settings used
All the results produced here are generated with (1) the raw geolocator data, (2) the labeled files of pressure and light and (3) the parameters listed below.
Show code
kable(gpr) %>% scroll_box(width = "100%")
| gdl_id | crop_start | crop_end | thr_dur | extent_N | extent_W | extent_S | extent_E | map_scale | map_max_sample | map_margin | prob_map_s | prob_map_s_calib | prob_map_thr | shift_k | kernel_adjust | calib_lon | calib_lat | calib_1_start | calib_1_end | calib_2_start | calib_2_end | calib_2_lon | calib_2_lat | prob_light_w | thr_prob_percentile | thr_gs | RingNo | scientific_name | common_name | mass | wing_span | Color |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 26IM | 2020-07-14 | 2021-05-28 | 24 | 51 | -18 | 4 | 16 | 5 | 300 | 30 | 1 | 2 | 0.9 | 0 | 1.4 | 8.705503 | 46.55076 | 2020-07-14 | 2020-09-17 | 2021-05-03 | 2021-05-28 | NA | NA | 0.1 | 0.9 | 120 | N643042 | Oenanthe oenanthe | Northern wheatear | NA | NA | NA |
Pressure timeserie
The labeling of pressure data is illustrated with this figure. The black dots indicates the pressure datapoint not considered in the matching. Each stationary period is illustrated by a different colored line.
Show code
pressure_na <- pam$pressure %>%
mutate(obs = ifelse(isoutliar | sta_id == 0, NA, obs))
p <- ggplot() +
geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
geom_point(data = subset(pam$pressure, isoutliar), aes(x = date, y = obs), colour = "black") +
# geom_line(data = pressure_na, aes(x = date, y = obs, color = factor(sta_id)), size = 0.5) +
geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = pressure0, col = factor(sta_id))) +
theme_bw() +
scale_colour_manual(values = col) +
scale_y_continuous(name = "Pressure(hPa)")
ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)
Light
Show code
raw_geolight <- pam$light %>%
transmute(
Date = date,
Light = obs
)
lightImage(tagdata = raw_geolight, offset = 0)
tsimagePoints(twl$twilight,
offset = 0, pch = 16, cex = 1.2,
col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
abline(v = gpr$calib_2_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_2_end, lty = 2, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_end, lty = 2, col = "firebrick", lwd = 1.5)
The probability map resulting from light data alone can be seen below.
Show code
li_s <- list()
l <- leaflet(width = "100%") %>%
addProviderTiles(providers$Stamen.TerrainBackground) %>%
addFullscreenControl()
for (i_r in seq_len(length(light_prob))) {
i_s <- metadata(light_prob[[i_r]])$sta_id
info <- pam$sta[pam$sta$sta_id == i_s, ]
info_str <- paste0(i_s, " | ", info$start, "->", info$end)
li_s <- append(li_s, info_str)
l <- l %>% addRasterImage(light_prob[[i_r]], opacity = 0.8, colors = "OrRd", group = info_str)
}
l %>%
addCircles(lng = gpr$calib_lon, lat = gpr$calib_lat, color = "black", opacity = 1) %>%
addLayersControl(
overlayGroups = li_s,
options = layersControlOptions(collapsed = FALSE)
) %>%
hideGroup(tail(li_s, length(li_s) - 1))
Light vs Pressure
We can compare light and pressure location at long stationary stopover (>5 days). By assuming the best match of the pressure to be the truth, we can plot the histogram of the zenith angle and compare to the fit of kernel density at the calibration site.
Show code
raw_geolight <- pam$light %>%
transmute(
Date = date,
Light = obs
)
dur <- unlist(lapply(static_prob_marginal, function(x) difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1], units = "days" )))
long_id <- which(dur>5 & seq(1,length(dur))<=length(shortest_path_timeserie))
par(mfrow = c(2, 3))
for (i_s in long_id){
twl_fl <- twl %>%
filter(!deleted) %>%
filter(twilight>shortest_path_timeserie[[i_s]]$date[1] & twilight<tail(shortest_path_timeserie[[i_s]]$date,1))
sun <- solar(twl_fl$twilight)
z_i <- refracted(zenith(sun, shortest_path_timeserie[[i_s]]$lon[1], shortest_path_timeserie[[i_s]]$lat[1]))
hist(z_i, freq = F, main = paste0("sta_id=",i_s, " | ",nrow(twl_fl),"twls"))
lines(fit_z, col = "red")
xlab("Zenith angle")
}
Similarly, we can plot the line of sunrise/sunset at the best match of pressure (yellow line) and compare to the raw and labeled light data.
Show code
lightImage(
tagdata = raw_geolight,
offset = gpr$shift_k / 60 / 60
)
tsimagePoints(twl$twilight,
offset = gpr$shift_k / 60 / 60, pch = 16, cex = 1.2,
col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
for (ts in shortest_path_timeserie){
if (!is.null(ts)){
twl_fl <- twl %>%
filter(twilight>ts$date[1] & twilight<tail(ts$date,1))
if (nrow(twl_fl)>0){
tsimageDeploymentLines(twl_fl$twilight,
lon = ts$lon[1], ts$lat[1],
offset = gpr$shift_k / 60 / 60, lwd = 3,col = adjustcolor("orange", alpha.f = 0.5))
}
}
}
GeoPressureViz
To visualize the path on GeoPressureViz, you will need to also load the pressure and light probability map and align them first with the code below.
Show code
sta_marginal <- unlist(lapply(static_prob_marginal, function(x) raster::metadata(x)$sta_id))
sta_pres <- unlist(lapply(pressure_prob, function(x) raster::metadata(x)$sta_id))
sta_light <- unlist(lapply(light_prob, function(x) raster::metadata(x)$sta_id))
pressure_prob <- pressure_prob[sta_pres %in% sta_marginal]
light_prob <- light_prob[sta_light %in% sta_marginal]
The code below will open with the shortest path computed with the graph approach.
Show code
geopressureviz <- list(
pam_data = pam,
static_prob = static_prob,
static_prob_marginal = static_prob_marginal,
pressure_prob = pressure_prob,
light_prob = light_prob,
pressure_timeserie = shortest_path_timeserie
)
save(geopressureviz, file = "~/geopressureviz.RData")
shiny::runApp(system.file("geopressureviz", package = "GeoPressureR"),
launch.browser = getOption("browser")
)
Stationay period information
| start | end | sta_id |
|---|---|---|
| 2020-07-14 00:00:00 | 2020-09-16 18:00:00 | 1 |
| 2020-09-17 02:30:00 | 2020-09-17 17:30:00 | 2 |
| 2020-09-18 00:00:00 | 2020-09-18 20:00:00 | 3 |
| 2020-09-18 22:30:00 | 2020-09-19 20:30:00 | 4 |
| 2020-09-19 22:00:00 | 2020-09-20 00:30:00 | 5 |
| 2020-09-20 06:30:00 | 2020-09-21 18:00:00 | 6 |
| 2020-09-22 03:30:00 | 2020-09-22 18:00:00 | 7 |
| 2020-09-23 03:00:00 | 2020-09-23 18:30:00 | 8 |
| 2020-09-23 22:30:00 | 2020-09-25 17:30:00 | 9 |
| 2020-09-26 04:30:00 | 2020-09-26 20:30:00 | 10 |
| 2020-09-26 22:00:00 | 2020-10-05 18:00:00 | 11 |
| 2020-10-06 04:00:00 | 2020-10-06 18:00:00 | 12 |
| 2020-10-07 06:30:00 | 2020-10-07 18:00:00 | 13 |
| 2020-10-08 06:00:00 | 2020-10-08 19:00:00 | 14 |
| 2020-10-09 05:30:00 | 2020-10-09 18:00:00 | 15 |
| 2020-10-09 18:30:00 | 2020-10-10 02:00:00 | 16 |
| 2020-10-10 05:00:00 | 2020-10-10 22:30:00 | 17 |
| 2020-10-10 23:30:00 | 2021-04-01 18:30:00 | 18 |
| 2021-04-02 05:00:00 | 2021-04-02 18:30:00 | 19 |
| 2021-04-03 07:30:00 | 2021-04-03 18:30:00 | 20 |
| 2021-04-04 16:00:00 | 2021-04-04 20:30:00 | 21 |
| 2021-04-04 22:00:00 | 2021-04-05 00:00:00 | 22 |
| 2021-04-05 04:00:00 | 2021-04-06 19:00:00 | 23 |
| 2021-04-07 01:00:00 | 2021-04-07 05:00:00 | 24 |
| 2021-04-07 07:00:00 | 2021-04-21 18:00:00 | 25 |
| 2021-04-21 20:00:00 | 2021-04-24 18:30:00 | 26 |
| 2021-04-25 04:30:00 | 2021-04-25 08:00:00 | 27 |
| 2021-04-25 11:00:00 | 2021-04-25 13:30:00 | 28 |
| 2021-04-25 15:30:00 | 2021-04-25 20:00:00 | 29 |
| 2021-04-25 21:30:00 | 2021-04-30 18:30:00 | 30 |
| 2021-05-01 01:30:00 | 2021-05-02 19:30:00 | 31 |
| 2021-05-03 01:00:00 | 2021-05-27 23:30:00 | 32 |